Results 21 to 30 of about 148,752 (225)

Data Simulation by Resampling—A Practical Data Augmentation Algorithm for Periodical Signal Analysis-Based Fault Diagnosis

open access: yesIEEE Access, 2019
In recent years, machine learning and deep learning based fault diagnosis methods have been studied, however, most of them remain at the experimental stage mainly because of two obstacles, briefly, a) inadequate faulty examples and b) various working ...
Tianhao Hu, Tang Tang, Ming Chen
doaj   +1 more source

Teknik Resampling untuk Mengatasi Ketidakseimbangan Kelas pada Klasifikasi Penyakit Diabetes Menggunakan C4.5, Random Forest, dan SVM

open access: yesTechno.Com, 2021
Penderita diabetes di seluruh dunia terus mengalami peningkatan dengan angka kematian sebesar 4,6 juta pada tahun 2011 dan diperkirakan akan terus meningkat secara global menjadi 552 juta pada tahun 2030.
Wahyu Nugraha, Raja Sabaruddin
doaj   +1 more source

Resampling images in Fourier domain [PDF]

open access: yes, 2014
When simulating sky images, one often takes a galaxy image $F(x)$ defined by a set of pixelized samples and an interpolation kernel, and then wants to produce a new sampled image representing this galaxy as it would appear with a different point-spread ...
Bernstein, Gary M., Gruen, Daniel
core   +1 more source

Fusing photovoltaic data for improved confidence intervals

open access: yesAIMS Energy, 2017
Characterizing and testing photovoltaic modules requires carefully made measurements on important variables such as the power output under standard conditions.
Ansgar Steland
doaj   +1 more source

The Influence of Unbalanced Economic Data on Feature Selection and Quality of Classifiers

open access: yesFolia Oeconomica Stetinensia, 2020
Research background: The successful learning of classifiers depends on the quality of data. Modeling is especially difficult when the data are unbalanced or contain many irrelevant variables. This is the case in many applications.
Kubus Mariusz
doaj   +1 more source

Bootstrap for neural model selection [PDF]

open access: yes, 2002
Bootstrap techniques (also called resampling computation techniques) have introduced new advances in modeling and model evaluation. Using resampling methods to construct a series of new samples which are based on the original data set, allows to estimate
Cottrell, Marie   +2 more
core   +3 more sources

Testing methods for using high‐resolution satellite imagery to monitor polar bear abundance and distribution

open access: yesWildlife Society Bulletin, 2015
High‐resolution satellite imagery is a promising tool for providing coarse information about polar species abundance and distribution, but current applications are limited.
Michelle A. LaRue   +6 more
doaj   +1 more source

Toward a principled sampling theory for quasi-orders

open access: yesFrontiers in Psychology, 2016
Quasi-orders, that is, reflexive and transitive binary relations, have numerous applications. In educational theories, the dependencies of mastery among the problems of a test can be modeled by quasi-orders.
Ali Ünlü, Martin Schrepp
doaj   +1 more source

COMPARISON OF RESAMPLING EFFICIENCY LEVELS OF JACKKNIFE AND DOUBLE JACKKNIFE IN PATH ANALYSIS

open access: yesBarekeng, 2023
The assumption of normality is often not fulfilled, this causes the estimation of the resulting parameters to be less efficient. The problem with assuming that normality is not satisfied can be overcome by resampling. The use of resampling allows data to
M. Fikar Papalia   +2 more
doaj   +1 more source

Dual Mesh Resampling

open access: yesGraphical Models, 2002
Summary: The dual of a 2-manifold polygonal mesh without boundary is commonly defined as another mesh with the same topology (genus) but different connectivity (vertex--face incidence), in which faces and vertices occupy complementary locations and the position of each dual vertex is computed as the center of mass (barycenter or centroid) of the ...
openaire   +2 more sources

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